scholarly journals Optimization of the fuel economy and emissions for plug in hybrid electric recreational boat energy management strategy using genetic algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 792
Author(s):  
Jabar Siti Norbakyah ◽  
Abdul Rahman Salisa
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Conventional Vehicle ◽  
Cycle Simulation ◽  
Water Transportation ◽  
Hybrid Electric

<span>Today, the transportation sector has undergone a change from conventional vehicle to hybrid electric vehicle especially land-based with the aim to reduce fuel consumption and emissions. However, water transportation is also one of the contributors of excessive use of fuel and emissions. Therefore, water transport needs changes as it has been done on land transport, especially cars. In this paper, plug in hybrid electric recreational boat (PHERB) is introduced. PHERB is a special model because in PHERB powertrain configuration, it only needed one EM compared to existing configuration with energy management strategy (EMS).  In this work, the optimal EMS for PHERB are presented via genetic algorithm (GA). To estimate the fuel economy and emissions, the model of PHERB is employed numerically in the MATLAB/SIMULINK environment with a special EMS using Kuala Terengganu (KT) river driving cycle. Simulation result of PHERB optimization using GA improve to 15% for KT river driving cycles without violating the PHERB performance.</span>

scholarly journals A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Pei Li ◽  
Jun Yan ◽  
Qunzhang Tu ◽  
Ming Pan ◽  
Jinhong Xue
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Method ◽  
Energy Management Strategy ◽  
Quantum Genetic Algorithm ◽  
Hybrid Electric

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.

Multi-objective real-time optimization energy management strategy for plug-in hybrid electric vehicle

2018 ◽  
Vol 233 (5) ◽  
pp. 1067-1080 ◽  
Author(s):  
Siyu Du ◽  
Yiyong Yang ◽  
Congzhi Liu ◽  
Fahad Muhammad
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Improve Fuel Economy ◽  
Hybrid Electric ◽  
Multiple Operation ◽  
Real Time Optimization

Plug-in hybrid electric vehicle provides remarkable results for emission reduction and fuel improvement in the current driving cycles. With the appropriate energy management strategy, the torque can be split by switching of multiple operation modes to improve fuel economy. However, in the process, not only the noticeable jerk or torque fluctuation, which may result in vibration of the drivetrain and unpleasant driving sensation, but also the frequent motor-start-engine process would be triggered, which is accompanied by extra fuel consumption and abrasion of the clutch. Therefore, high attention should be paid to reduce the excess operating times of the motor-start-engine process and take advantage of multiple operation modes to improve fuel economy in plug-in hybrid electric vehicle. To solve this problem, a multi-objective real-time optimization energy management strategy is proposed. First, the motor-start-engine dynamic model of 2-degree-of-freedom is established. Then, the motor-start-engine process is analyzed based on a large number of real-world data, and the cost of the motor-start-engine process is quantified for optimization. What’s more, the optimal torque distribution is realized through the powertrain system. Finally, the proposed strategy is verified by the simulation and experiment platform. Results show that the proposed strategy can greatly improve fuel economy, thereby reducing the excess operating times of the motor-start-engine process.

Fuzzy Torque Distribution Control for a Parallel Hybrid Electric Vehicle

2001 ◽  
Author(s):  
Jong-Seob Won ◽  
Reza Langari
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Torque Distribution ◽  
Parallel Hybrid Electric Vehicle ◽  
Cycle Simulation ◽  
Mode Of Operation ◽  
Parallel Hybrid ◽  
Hybrid Electric

Abstract A fuzzy torque distribution controller for energy management (and emission control) of a parallel-hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy as well as emissions.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

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